Overview

Dataset statistics

Number of variables27
Number of observations181
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.3 KiB
Average record size in memory216.7 B

Variable types

Numeric17
Categorical10

Alerts

symboling is highly overall correlated with normalized_losses and 2 other fieldsHigh correlation
normalized_losses is highly overall correlated with symbolingHigh correlation
wheel_base is highly overall correlated with symboling and 8 other fieldsHigh correlation
length is highly overall correlated with wheel_base and 9 other fieldsHigh correlation
width is highly overall correlated with wheel_base and 10 other fieldsHigh correlation
height is highly overall correlated with symboling and 3 other fieldsHigh correlation
curb_weight is highly overall correlated with wheel_base and 10 other fieldsHigh correlation
engine_size is highly overall correlated with wheel_base and 12 other fieldsHigh correlation
bore is highly overall correlated with wheel_base and 9 other fieldsHigh correlation
stroke is highly overall correlated with make and 1 other fieldsHigh correlation
compression_ratio is highly overall correlated with make and 4 other fieldsHigh correlation
horsepower is highly overall correlated with length and 9 other fieldsHigh correlation
peak_rpm is highly overall correlated with fuel_typeHigh correlation
city_mpg is highly overall correlated with length and 7 other fieldsHigh correlation
highway_mpg is highly overall correlated with length and 8 other fieldsHigh correlation
price is highly overall correlated with wheel_base and 8 other fieldsHigh correlation
make is highly overall correlated with width and 10 other fieldsHigh correlation
fuel_type is highly overall correlated with compression_ratio and 2 other fieldsHigh correlation
aspiration is highly overall correlated with compression_ratio and 1 other fieldsHigh correlation
num_of_doors is highly overall correlated with body_styleHigh correlation
body_style is highly overall correlated with height and 1 other fieldsHigh correlation
drive_wheels is highly overall correlated with makeHigh correlation
engine_location is highly overall correlated with wheel_base and 4 other fieldsHigh correlation
engine_type is highly overall correlated with engine_size and 2 other fieldsHigh correlation
num_of_cylinders is highly overall correlated with width and 7 other fieldsHigh correlation
fuel_system is highly overall correlated with compression_ratio and 3 other fieldsHigh correlation
fuel_type is highly imbalanced (53.3%)Imbalance
engine_location is highly imbalanced (87.8%)Imbalance
num_of_cylinders is highly imbalanced (57.3%)Imbalance
ID is uniformly distributedUniform
ID has unique valuesUnique
symboling has 59 (32.6%) zerosZeros

Reproduction

Analysis started2023-09-22 18:49:10.525188
Analysis finished2023-09-22 18:49:27.724723
Duration17.2 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91
Minimum1
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:27.779991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q146
median91
Q3136
95-th percentile172
Maximum181
Range180
Interquartile range (IQR)90

Descriptive statistics

Standard deviation52.394338
Coefficient of variation (CV)0.57576196
Kurtosis-1.2
Mean91
Median Absolute Deviation (MAD)45
Skewness0
Sum16471
Variance2745.1667
MonotonicityStrictly increasing
2023-09-23T00:19:27.845074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
115 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
121 1
 
0.6%
122 1
 
0.6%
123 1
 
0.6%
124 1
 
0.6%
Other values (171) 171
94.5%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
181 1
0.6%
180 1
0.6%
179 1
0.6%
178 1
0.6%
177 1
0.6%
176 1
0.6%
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%

symboling
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85082873
Minimum-2
Maximum3
Zeros59
Zeros (%)32.6%
Negative23
Negative (%)12.7%
Memory size1.5 KiB
2023-09-23T00:19:27.905560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2670446
Coefficient of variation (CV)1.4891888
Kurtosis-0.79306123
Mean0.85082873
Median Absolute Deviation (MAD)1
Skewness0.23452382
Sum154
Variance1.6054021
MonotonicityNot monotonic
2023-09-23T00:19:27.950707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 59
32.6%
1 45
24.9%
2 28
15.5%
3 26
14.4%
-1 21
 
11.6%
-2 2
 
1.1%
ValueCountFrequency (%)
-2 2
 
1.1%
-1 21
 
11.6%
0 59
32.6%
1 45
24.9%
2 28
15.5%
3 26
14.4%
ValueCountFrequency (%)
3 26
14.4%
2 28
15.5%
1 45
24.9%
0 59
32.6%
-1 21
 
11.6%
-2 2
 
1.1%

normalized_losses
Real number (ℝ)

Distinct51
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.76243
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:28.006221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile77
Q198
median115
Q3134
95-th percentile168
Maximum256
Range191
Interquartile range (IQR)36

Descriptive statistics

Standard deviation31.682872
Coefficient of variation (CV)0.26454767
Kurtosis1.9000768
Mean119.76243
Median Absolute Deviation (MAD)19
Skewness1.0463132
Sum21677
Variance1003.8044
MonotonicityNot monotonic
2023-09-23T00:19:28.079527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115 37
20.4%
161 9
 
5.0%
91 7
 
3.9%
150 6
 
3.3%
104 6
 
3.3%
134 6
 
3.3%
95 5
 
2.8%
85 5
 
2.8%
102 5
 
2.8%
65 5
 
2.8%
Other values (41) 90
49.7%
ValueCountFrequency (%)
65 5
2.8%
74 4
2.2%
77 1
 
0.6%
78 1
 
0.6%
81 2
 
1.1%
83 3
1.7%
85 5
2.8%
87 1
 
0.6%
89 2
 
1.1%
90 1
 
0.6%
ValueCountFrequency (%)
256 1
 
0.6%
231 1
 
0.6%
197 2
 
1.1%
194 2
 
1.1%
192 1
 
0.6%
188 1
 
0.6%
186 1
 
0.6%
168 3
 
1.7%
164 2
 
1.1%
161 9
5.0%

make
Categorical

Distinct22
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
toyota
28 
nissan
16 
mazda
16 
subaru
12 
mitsubishi
12 
Other values (17)
97 

Length

Max length13
Median length11
Mean length6.5966851
Min length3

Characters and Unicode

Total characters1194
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowtoyota
2nd rowbmw
3rd rowrenault
4th rowpeugot
5th rowvolvo

Common Values

ValueCountFrequency (%)
toyota 28
15.5%
nissan 16
 
8.8%
mazda 16
 
8.8%
subaru 12
 
6.6%
mitsubishi 12
 
6.6%
honda 12
 
6.6%
volkswagen 11
 
6.1%
dodge 9
 
5.0%
volvo 9
 
5.0%
peugot 9
 
5.0%
Other values (12) 47
26.0%

Length

2023-09-23T00:19:28.141229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 28
15.5%
nissan 16
 
8.8%
mazda 16
 
8.8%
subaru 12
 
6.6%
mitsubishi 12
 
6.6%
honda 12
 
6.6%
volkswagen 11
 
6.1%
dodge 9
 
5.0%
volvo 9
 
5.0%
peugot 9
 
5.0%
Other values (12) 47
26.0%

Most occurring characters

ValueCountFrequency (%)
a 140
 
11.7%
o 134
 
11.2%
s 98
 
8.2%
t 88
 
7.4%
e 77
 
6.4%
u 66
 
5.5%
n 65
 
5.4%
i 59
 
4.9%
d 59
 
4.9%
m 51
 
4.3%
Other values (15) 357
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1183
99.1%
Dash Punctuation 11
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 140
11.8%
o 134
 
11.3%
s 98
 
8.3%
t 88
 
7.4%
e 77
 
6.5%
u 66
 
5.6%
n 65
 
5.5%
i 59
 
5.0%
d 59
 
5.0%
m 51
 
4.3%
Other values (14) 346
29.2%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1183
99.1%
Common 11
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 140
11.8%
o 134
 
11.3%
s 98
 
8.3%
t 88
 
7.4%
e 77
 
6.5%
u 66
 
5.6%
n 65
 
5.5%
i 59
 
5.0%
d 59
 
5.0%
m 51
 
4.3%
Other values (14) 346
29.2%
Common
ValueCountFrequency (%)
- 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 140
 
11.7%
o 134
 
11.2%
s 98
 
8.2%
t 88
 
7.4%
e 77
 
6.4%
u 66
 
5.5%
n 65
 
5.4%
i 59
 
4.9%
d 59
 
4.9%
m 51
 
4.3%
Other values (15) 357
29.9%

fuel_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
gas
163 
diesel
18 

Length

Max length6
Median length3
Mean length3.2983425
Min length3

Characters and Unicode

Total characters597
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowdiesel
5th rowgas

Common Values

ValueCountFrequency (%)
gas 163
90.1%
diesel 18
 
9.9%

Length

2023-09-23T00:19:28.192028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T00:19:28.252796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
gas 163
90.1%
diesel 18
 
9.9%

Most occurring characters

ValueCountFrequency (%)
s 181
30.3%
g 163
27.3%
a 163
27.3%
e 36
 
6.0%
d 18
 
3.0%
i 18
 
3.0%
l 18
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 597
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 181
30.3%
g 163
27.3%
a 163
27.3%
e 36
 
6.0%
d 18
 
3.0%
i 18
 
3.0%
l 18
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 597
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 181
30.3%
g 163
27.3%
a 163
27.3%
e 36
 
6.0%
d 18
 
3.0%
i 18
 
3.0%
l 18
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 181
30.3%
g 163
27.3%
a 163
27.3%
e 36
 
6.0%
d 18
 
3.0%
i 18
 
3.0%
l 18
 
3.0%

aspiration
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
std
147 
turbo
34 

Length

Max length5
Median length3
Mean length3.3756906
Min length3

Characters and Unicode

Total characters611
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowturbo
5th rowstd

Common Values

ValueCountFrequency (%)
std 147
81.2%
turbo 34
 
18.8%

Length

2023-09-23T00:19:28.300198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T00:19:28.354047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
std 147
81.2%
turbo 34
 
18.8%

Most occurring characters

ValueCountFrequency (%)
t 181
29.6%
s 147
24.1%
d 147
24.1%
u 34
 
5.6%
r 34
 
5.6%
b 34
 
5.6%
o 34
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 611
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 181
29.6%
s 147
24.1%
d 147
24.1%
u 34
 
5.6%
r 34
 
5.6%
b 34
 
5.6%
o 34
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 611
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 181
29.6%
s 147
24.1%
d 147
24.1%
u 34
 
5.6%
r 34
 
5.6%
b 34
 
5.6%
o 34
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 181
29.6%
s 147
24.1%
d 147
24.1%
u 34
 
5.6%
r 34
 
5.6%
b 34
 
5.6%
o 34
 
5.6%

num_of_doors
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
four
102 
two
77 
?
 
2

Length

Max length4
Median length4
Mean length3.5414365
Min length1

Characters and Unicode

Total characters641
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfour
2nd rowtwo
3rd rowfour
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 102
56.4%
two 77
42.5%
? 2
 
1.1%

Length

2023-09-23T00:19:28.400846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T00:19:28.454293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
four 102
56.4%
two 77
42.5%
2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
o 179
27.9%
f 102
15.9%
u 102
15.9%
r 102
15.9%
t 77
12.0%
w 77
12.0%
? 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 639
99.7%
Other Punctuation 2
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 179
28.0%
f 102
16.0%
u 102
16.0%
r 102
16.0%
t 77
12.1%
w 77
12.1%
Other Punctuation
ValueCountFrequency (%)
? 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 639
99.7%
Common 2
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 179
28.0%
f 102
16.0%
u 102
16.0%
r 102
16.0%
t 77
12.1%
w 77
12.1%
Common
ValueCountFrequency (%)
? 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 179
27.9%
f 102
15.9%
u 102
15.9%
r 102
15.9%
t 77
12.0%
w 77
12.0%
? 2
 
0.3%

body_style
Categorical

Distinct5
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
sedan
83 
hatchback
62 
wagon
22 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6574586
Min length5

Characters and Unicode

Total characters1205
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhatchback
2nd rowsedan
3rd rowwagon
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 83
45.9%
hatchback 62
34.3%
wagon 22
 
12.2%
hardtop 8
 
4.4%
convertible 6
 
3.3%

Length

2023-09-23T00:19:28.500321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T00:19:28.559744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
sedan 83
45.9%
hatchback 62
34.3%
wagon 22
 
12.2%
hardtop 8
 
4.4%
convertible 6
 
3.3%

Most occurring characters

ValueCountFrequency (%)
a 237
19.7%
h 132
11.0%
c 130
10.8%
n 111
9.2%
e 95
7.9%
d 91
 
7.6%
s 83
 
6.9%
t 76
 
6.3%
b 68
 
5.6%
k 62
 
5.1%
Other values (8) 120
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1205
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 237
19.7%
h 132
11.0%
c 130
10.8%
n 111
9.2%
e 95
7.9%
d 91
 
7.6%
s 83
 
6.9%
t 76
 
6.3%
b 68
 
5.6%
k 62
 
5.1%
Other values (8) 120
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1205
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 237
19.7%
h 132
11.0%
c 130
10.8%
n 111
9.2%
e 95
7.9%
d 91
 
7.6%
s 83
 
6.9%
t 76
 
6.3%
b 68
 
5.6%
k 62
 
5.1%
Other values (8) 120
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 237
19.7%
h 132
11.0%
c 130
10.8%
n 111
9.2%
e 95
7.9%
d 91
 
7.6%
s 83
 
6.9%
t 76
 
6.3%
b 68
 
5.6%
k 62
 
5.1%
Other values (8) 120
10.0%

drive_wheels
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
fwd
108 
rwd
65 
4wd
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters543
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfwd
2nd rowrwd
3rd rowfwd
4th rowrwd
5th rowrwd

Common Values

ValueCountFrequency (%)
fwd 108
59.7%
rwd 65
35.9%
4wd 8
 
4.4%

Length

2023-09-23T00:19:28.617606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T00:19:28.668640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
fwd 108
59.7%
rwd 65
35.9%
4wd 8
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w 181
33.3%
d 181
33.3%
f 108
19.9%
r 65
 
12.0%
4 8
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 535
98.5%
Decimal Number 8
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 181
33.8%
d 181
33.8%
f 108
20.2%
r 65
 
12.1%
Decimal Number
ValueCountFrequency (%)
4 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 535
98.5%
Common 8
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 181
33.8%
d 181
33.8%
f 108
20.2%
r 65
 
12.1%
Common
ValueCountFrequency (%)
4 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 181
33.3%
d 181
33.3%
f 108
19.9%
r 65
 
12.0%
4 8
 
1.5%

engine_location
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
front
178 
rear
 
3

Length

Max length5
Median length5
Mean length4.9834254
Min length4

Characters and Unicode

Total characters902
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 178
98.3%
rear 3
 
1.7%

Length

2023-09-23T00:19:28.714445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T00:19:28.764337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
front 178
98.3%
rear 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
r 184
20.4%
f 178
19.7%
o 178
19.7%
n 178
19.7%
t 178
19.7%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 902
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 184
20.4%
f 178
19.7%
o 178
19.7%
n 178
19.7%
t 178
19.7%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 902
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 184
20.4%
f 178
19.7%
o 178
19.7%
n 178
19.7%
t 178
19.7%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 184
20.4%
f 178
19.7%
o 178
19.7%
n 178
19.7%
t 178
19.7%
e 3
 
0.3%
a 3
 
0.3%

wheel_base
Real number (ℝ)

Distinct52
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.729282
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:28.813633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile91.3
Q194.5
median96.9
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.1161965
Coefficient of variation (CV)0.061949165
Kurtosis1.0157942
Mean98.729282
Median Absolute Deviation (MAD)2.6
Skewness1.0467193
Sum17870
Variance37.40786
MonotonicityNot monotonic
2023-09-23T00:19:28.877327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.7 17
 
9.4%
94.5 15
 
8.3%
95.7 11
 
6.1%
96.5 8
 
4.4%
97.3 6
 
3.3%
98.8 6
 
3.3%
98.4 6
 
3.3%
96.3 6
 
3.3%
100.4 6
 
3.3%
102.4 5
 
2.8%
Other values (42) 95
52.5%
ValueCountFrequency (%)
86.6 2
 
1.1%
88.4 1
 
0.6%
88.6 2
 
1.1%
89.5 3
 
1.7%
91.3 2
 
1.1%
93 1
 
0.6%
93.1 5
 
2.8%
93.3 1
 
0.6%
93.7 17
9.4%
94.3 1
 
0.6%
ValueCountFrequency (%)
120.9 1
 
0.6%
115.6 2
 
1.1%
114.2 3
1.7%
113 2
 
1.1%
112 1
 
0.6%
110 3
1.7%
109.1 5
2.8%
108 1
 
0.6%
107.9 5
2.8%
106.7 1
 
0.6%

length
Real number (ℝ)

Distinct72
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.17403
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:28.944820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.3
Q1166.8
median173.2
Q3183.1
95-th percentile197
Maximum208.1
Range67
Interquartile range (IQR)16.3

Descriptive statistics

Standard deviation12.336569
Coefficient of variation (CV)0.070828979
Kurtosis-0.0040332166
Mean174.17403
Median Absolute Deviation (MAD)6.9
Skewness0.16648849
Sum31525.5
Variance152.19093
MonotonicityNot monotonic
2023-09-23T00:19:29.008437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 14
 
7.7%
188.8 9
 
5.0%
171.7 6
 
3.3%
186.7 6
 
3.3%
176.2 6
 
3.3%
177.8 6
 
3.3%
166.3 6
 
3.3%
175.6 5
 
2.8%
172 5
 
2.8%
186.6 5
 
2.8%
Other values (62) 113
62.4%
ValueCountFrequency (%)
141.1 1
 
0.6%
144.6 2
 
1.1%
150 2
 
1.1%
155.9 1
 
0.6%
156.9 1
 
0.6%
157.1 1
 
0.6%
157.3 14
7.7%
157.9 1
 
0.6%
158.7 2
 
1.1%
158.8 1
 
0.6%
ValueCountFrequency (%)
208.1 1
 
0.6%
202.6 2
1.1%
199.6 2
1.1%
199.2 1
 
0.6%
198.9 3
1.7%
197 1
 
0.6%
193.8 1
 
0.6%
192.7 3
1.7%
191.7 1
 
0.6%
190.9 2
1.1%

width
Real number (ℝ)

Distinct43
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.943646
Minimum60.3
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:29.073784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.2
median65.5
Q366.6
95-th percentile70.5
Maximum72
Range11.7
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation2.1461143
Coefficient of variation (CV)0.032544671
Kurtosis0.61327037
Mean65.943646
Median Absolute Deviation (MAD)1.3
Skewness0.85445819
Sum11935.8
Variance4.6058066
MonotonicityNot monotonic
2023-09-23T00:19:29.142568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
66.5 22
 
12.2%
63.8 21
 
11.6%
65.4 15
 
8.3%
64.4 8
 
4.4%
63.6 8
 
4.4%
68.4 8
 
4.4%
65.5 7
 
3.9%
65.2 7
 
3.9%
64.2 6
 
3.3%
64 6
 
3.3%
Other values (33) 73
40.3%
ValueCountFrequency (%)
60.3 1
 
0.6%
61.8 1
 
0.6%
62.5 1
 
0.6%
63.4 1
 
0.6%
63.6 8
 
4.4%
63.8 21
11.6%
63.9 3
 
1.7%
64 6
 
3.3%
64.1 2
 
1.1%
64.2 6
 
3.3%
ValueCountFrequency (%)
72 1
 
0.6%
71.7 3
1.7%
71.4 3
1.7%
70.9 1
 
0.6%
70.6 1
 
0.6%
70.5 1
 
0.6%
70.3 3
1.7%
69.6 2
1.1%
68.9 4
2.2%
68.8 1
 
0.6%

height
Real number (ℝ)

Distinct48
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.709945
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:29.204829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q151.6
median54.1
Q355.5
95-th percentile58.3
Maximum59.8
Range12
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation2.4977346
Coefficient of variation (CV)0.046504137
Kurtosis-0.44896264
Mean53.709945
Median Absolute Deviation (MAD)1.6
Skewness0.067837784
Sum9721.5
Variance6.2386783
MonotonicityNot monotonic
2023-09-23T00:19:29.271386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
50.8 12
 
6.6%
55.7 10
 
5.5%
54.1 10
 
5.5%
52 9
 
5.0%
55.5 9
 
5.0%
54.5 8
 
4.4%
51.6 7
 
3.9%
56.7 6
 
3.3%
50.2 6
 
3.3%
52.8 6
 
3.3%
Other values (38) 98
54.1%
ValueCountFrequency (%)
47.8 1
 
0.6%
48.8 2
 
1.1%
49.4 2
 
1.1%
49.6 4
 
2.2%
49.7 3
 
1.7%
50.2 6
3.3%
50.5 1
 
0.6%
50.6 5
2.8%
50.8 12
6.6%
51 1
 
0.6%
ValueCountFrequency (%)
59.8 2
 
1.1%
59.1 3
1.7%
58.7 4
2.2%
58.3 1
 
0.6%
57.5 2
 
1.1%
56.7 6
3.3%
56.5 2
 
1.1%
56.3 2
 
1.1%
56.2 2
 
1.1%
56.1 6
3.3%

curb_weight
Real number (ℝ)

Distinct155
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2564.1657
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:29.334966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1900
Q12190
median2420
Q32935
95-th percentile3515
Maximum4066
Range2578
Interquartile range (IQR)745

Descriptive statistics

Standard deviation523.48902
Coefficient of variation (CV)0.20415569
Kurtosis0.065088963
Mean2564.1657
Median Absolute Deviation (MAD)380
Skewness0.71768623
Sum464114
Variance274040.75
MonotonicityNot monotonic
2023-09-23T00:19:29.400852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.2%
3252 2
 
1.1%
3139 2
 
1.1%
1918 2
 
1.1%
2410 2
 
1.1%
2380 2
 
1.1%
2290 2
 
1.1%
2191 2
 
1.1%
2548 2
 
1.1%
1989 2
 
1.1%
Other values (145) 159
87.8%
ValueCountFrequency (%)
1488 1
0.6%
1713 1
0.6%
1819 1
0.6%
1837 1
0.6%
1874 1
0.6%
1876 2
1.1%
1889 1
0.6%
1890 1
0.6%
1900 1
0.6%
1905 1
0.6%
ValueCountFrequency (%)
4066 2
1.1%
3950 1
0.6%
3900 1
0.6%
3770 1
0.6%
3750 1
0.6%
3740 1
0.6%
3715 1
0.6%
3685 1
0.6%
3515 1
0.6%
3505 1
0.6%

engine_type
Categorical

Distinct6
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
ohc
128 
ohcf
15 
ohcv
13 
dohc
 
11
l
 
10

Length

Max length5
Median length3
Mean length3.1491713
Min length1

Characters and Unicode

Total characters570
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowohc
2nd rowohc
3rd rowohc
4th rowl
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 128
70.7%
ohcf 15
 
8.3%
ohcv 13
 
7.2%
dohc 11
 
6.1%
l 10
 
5.5%
rotor 4
 
2.2%

Length

2023-09-23T00:19:29.462143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T00:19:29.520944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ohc 128
70.7%
ohcf 15
 
8.3%
ohcv 13
 
7.2%
dohc 11
 
6.1%
l 10
 
5.5%
rotor 4
 
2.2%

Most occurring characters

ValueCountFrequency (%)
o 175
30.7%
h 167
29.3%
c 167
29.3%
f 15
 
2.6%
v 13
 
2.3%
d 11
 
1.9%
l 10
 
1.8%
r 8
 
1.4%
t 4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 570
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 175
30.7%
h 167
29.3%
c 167
29.3%
f 15
 
2.6%
v 13
 
2.3%
d 11
 
1.9%
l 10
 
1.8%
r 8
 
1.4%
t 4
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 570
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 175
30.7%
h 167
29.3%
c 167
29.3%
f 15
 
2.6%
v 13
 
2.3%
d 11
 
1.9%
l 10
 
1.8%
r 8
 
1.4%
t 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 175
30.7%
h 167
29.3%
c 167
29.3%
f 15
 
2.6%
v 13
 
2.3%
d 11
 
1.9%
l 10
 
1.8%
r 8
 
1.4%
t 4
 
0.7%

num_of_cylinders
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
four
140 
six
22 
five
 
9
two
 
4
eight
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.8950276
Min length3

Characters and Unicode

Total characters705
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.1%

Sample

1st rowfour
2nd rowsix
3rd rowfour
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 140
77.3%
six 22
 
12.2%
five 9
 
5.0%
two 4
 
2.2%
eight 4
 
2.2%
three 1
 
0.6%
twelve 1
 
0.6%

Length

2023-09-23T00:19:29.573102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T00:19:29.631523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
four 140
77.3%
six 22
 
12.2%
five 9
 
5.0%
two 4
 
2.2%
eight 4
 
2.2%
three 1
 
0.6%
twelve 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
f 149
21.1%
o 144
20.4%
r 141
20.0%
u 140
19.9%
i 35
 
5.0%
s 22
 
3.1%
x 22
 
3.1%
e 17
 
2.4%
v 10
 
1.4%
t 10
 
1.4%
Other values (4) 15
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 705
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 149
21.1%
o 144
20.4%
r 141
20.0%
u 140
19.9%
i 35
 
5.0%
s 22
 
3.1%
x 22
 
3.1%
e 17
 
2.4%
v 10
 
1.4%
t 10
 
1.4%
Other values (4) 15
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 705
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 149
21.1%
o 144
20.4%
r 141
20.0%
u 140
19.9%
i 35
 
5.0%
s 22
 
3.1%
x 22
 
3.1%
e 17
 
2.4%
v 10
 
1.4%
t 10
 
1.4%
Other values (4) 15
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 149
21.1%
o 144
20.4%
r 141
20.0%
u 140
19.9%
i 35
 
5.0%
s 22
 
3.1%
x 22
 
3.1%
e 17
 
2.4%
v 10
 
1.4%
t 10
 
1.4%
Other values (4) 15
 
2.1%

engine_size
Real number (ℝ)

Distinct43
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.90055
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:29.687464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q198
median120
Q3146
95-th percentile194
Maximum326
Range265
Interquartile range (IQR)48

Descriptive statistics

Standard deviation42.578438
Coefficient of variation (CV)0.33290269
Kurtosis5.2867129
Mean127.90055
Median Absolute Deviation (MAD)23
Skewness1.9509667
Sum23150
Variance1812.9234
MonotonicityNot monotonic
2023-09-23T00:19:29.748674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
122 15
 
8.3%
108 12
 
6.6%
92 12
 
6.6%
98 12
 
6.6%
97 11
 
6.1%
110 11
 
6.1%
90 9
 
5.0%
109 8
 
4.4%
141 6
 
3.3%
152 6
 
3.3%
Other values (33) 79
43.6%
ValueCountFrequency (%)
61 1
 
0.6%
70 3
 
1.7%
79 1
 
0.6%
80 1
 
0.6%
90 9
5.0%
91 5
2.8%
92 12
6.6%
97 11
6.1%
98 12
6.6%
103 1
 
0.6%
ValueCountFrequency (%)
326 1
 
0.6%
308 1
 
0.6%
304 1
 
0.6%
258 2
 
1.1%
234 2
 
1.1%
209 2
 
1.1%
194 3
1.7%
183 4
2.2%
181 6
3.3%
173 1
 
0.6%

fuel_system
Categorical

Distinct8
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
mpfi
81 
2bbl
58 
idi
18 
1bbl
10 
spdi
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.8950276
Min length3

Characters and Unicode

Total characters705
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.1%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowidi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 81
44.8%
2bbl 58
32.0%
idi 18
 
9.9%
1bbl 10
 
5.5%
spdi 9
 
5.0%
4bbl 3
 
1.7%
mfi 1
 
0.6%
spfi 1
 
0.6%

Length

2023-09-23T00:19:29.806425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T00:19:29.864550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 81
44.8%
2bbl 58
32.0%
idi 18
 
9.9%
1bbl 10
 
5.5%
spdi 9
 
5.0%
4bbl 3
 
1.7%
mfi 1
 
0.6%
spfi 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
b 142
20.1%
i 128
18.2%
p 91
12.9%
f 83
11.8%
m 82
11.6%
l 71
10.1%
2 58
8.2%
d 27
 
3.8%
1 10
 
1.4%
s 10
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 634
89.9%
Decimal Number 71
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 142
22.4%
i 128
20.2%
p 91
14.4%
f 83
13.1%
m 82
12.9%
l 71
11.2%
d 27
 
4.3%
s 10
 
1.6%
Decimal Number
ValueCountFrequency (%)
2 58
81.7%
1 10
 
14.1%
4 3
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 634
89.9%
Common 71
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 142
22.4%
i 128
20.2%
p 91
14.4%
f 83
13.1%
m 82
12.9%
l 71
11.2%
d 27
 
4.3%
s 10
 
1.6%
Common
ValueCountFrequency (%)
2 58
81.7%
1 10
 
14.1%
4 3
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 142
20.1%
i 128
18.2%
p 91
12.9%
f 83
11.8%
m 82
11.6%
l 71
10.1%
2 58
8.2%
d 27
 
3.8%
1 10
 
1.4%
s 10
 
1.4%

bore
Real number (ℝ)

Distinct36
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3387845
Minimum2.68
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:29.921331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.68
5-th percentile2.97
Q13.15
median3.33
Q33.59
95-th percentile3.78
Maximum3.94
Range1.26
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.26229471
Coefficient of variation (CV)0.078559939
Kurtosis-1.013091
Mean3.3387845
Median Absolute Deviation (MAD)0.25
Skewness0.025849572
Sum604.32
Variance0.068798514
MonotonicityNot monotonic
2023-09-23T00:19:29.981359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.62 21
 
11.6%
3.19 18
 
9.9%
3.15 13
 
7.2%
2.97 10
 
5.5%
3.03 10
 
5.5%
3.43 8
 
4.4%
3.46 7
 
3.9%
3.31 7
 
3.9%
3.78 7
 
3.9%
3.39 6
 
3.3%
Other values (26) 74
40.9%
ValueCountFrequency (%)
2.68 1
 
0.6%
2.91 6
3.3%
2.92 1
 
0.6%
2.97 10
5.5%
2.99 1
 
0.6%
3.01 4
 
2.2%
3.03 10
5.5%
3.05 5
2.8%
3.08 1
 
0.6%
3.13 1
 
0.6%
ValueCountFrequency (%)
3.94 1
 
0.6%
3.8 2
 
1.1%
3.78 7
 
3.9%
3.74 3
 
1.7%
3.7 5
 
2.8%
3.63 2
 
1.1%
3.62 21
11.6%
3.61 1
 
0.6%
3.6 1
 
0.6%
3.59 3
 
1.7%

stroke
Real number (ℝ)

Distinct34
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2761326
Minimum2.19
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:30.038974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.19
5-th percentile2.64
Q13.12
median3.35
Q33.46
95-th percentile3.64
Maximum4.17
Range1.98
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.30658779
Coefficient of variation (CV)0.093582228
Kurtosis1.3920991
Mean3.2761326
Median Absolute Deviation (MAD)0.16
Skewness-0.45846823
Sum592.98
Variance0.093996071
MonotonicityNot monotonic
2023-09-23T00:19:30.096962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3.4 17
 
9.4%
3.03 12
 
6.6%
3.15 12
 
6.6%
3.39 12
 
6.6%
3.23 12
 
6.6%
3.35 12
 
6.6%
2.64 11
 
6.1%
3.46 8
 
4.4%
3.29 7
 
3.9%
3.58 6
 
3.3%
Other values (24) 72
39.8%
ValueCountFrequency (%)
2.19 1
 
0.6%
2.36 1
 
0.6%
2.64 11
6.1%
2.68 2
 
1.1%
2.76 1
 
0.6%
2.8 1
 
0.6%
2.87 1
 
0.6%
2.9 3
 
1.7%
3.03 12
6.6%
3.07 6
3.3%
ValueCountFrequency (%)
4.17 2
 
1.1%
3.9 3
 
1.7%
3.86 4
2.2%
3.64 5
2.8%
3.58 6
3.3%
3.54 4
2.2%
3.52 5
2.8%
3.5 6
3.3%
3.47 4
2.2%
3.46 8
4.4%

compression_ratio
Real number (ℝ)

Distinct32
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.154254
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:30.156044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.5
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9823063
Coefficient of variation (CV)0.39218107
Kurtosis5.0778278
Mean10.154254
Median Absolute Deviation (MAD)0.4
Skewness2.582607
Sum1837.92
Variance15.858763
MonotonicityNot monotonic
2023-09-23T00:19:30.207687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 43
23.8%
9.4 21
11.6%
8.5 13
 
7.2%
9.5 12
 
6.6%
9.3 10
 
5.5%
8.7 9
 
5.0%
9.2 7
 
3.9%
7 6
 
3.3%
8 6
 
3.3%
8.6 5
 
2.8%
Other values (22) 49
27.1%
ValueCountFrequency (%)
7 6
3.3%
7.5 4
 
2.2%
7.6 4
 
2.2%
7.7 2
 
1.1%
7.8 1
 
0.6%
8 6
3.3%
8.1 2
 
1.1%
8.3 3
 
1.7%
8.4 3
 
1.7%
8.5 13
7.2%
ValueCountFrequency (%)
23 4
2.2%
22.7 1
 
0.6%
22.5 2
 
1.1%
22 1
 
0.6%
21.9 1
 
0.6%
21.5 4
2.2%
21 5
2.8%
11.5 1
 
0.6%
10.1 1
 
0.6%
10 2
 
1.1%

horsepower
Real number (ℝ)

Distinct57
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.0663
Minimum48
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:30.269600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q172
median95
Q3116
95-th percentile176
Maximum262
Range214
Interquartile range (IQR)44

Descriptive statistics

Standard deviation37.768094
Coefficient of variation (CV)0.36292339
Kurtosis1.3374009
Mean104.0663
Median Absolute Deviation (MAD)22
Skewness1.1559094
Sum18836
Variance1426.4289
MonotonicityNot monotonic
2023-09-23T00:19:30.330067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 16
 
8.8%
116 9
 
5.0%
69 8
 
4.4%
95 8
 
4.4%
70 8
 
4.4%
110 6
 
3.3%
88 6
 
3.3%
82 5
 
2.8%
145 5
 
2.8%
62 5
 
2.8%
Other values (47) 105
58.0%
ValueCountFrequency (%)
48 1
 
0.6%
52 2
 
1.1%
55 1
 
0.6%
56 1
 
0.6%
58 1
 
0.6%
60 1
 
0.6%
62 5
 
2.8%
64 1
 
0.6%
68 16
8.8%
69 8
4.4%
ValueCountFrequency (%)
262 1
 
0.6%
207 3
1.7%
200 1
 
0.6%
184 2
 
1.1%
182 2
 
1.1%
176 2
 
1.1%
175 1
 
0.6%
162 1
 
0.6%
161 2
 
1.1%
160 5
2.8%

peak_rpm
Real number (ℝ)

Distinct22
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5106.6298
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:30.382370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4200
Q14800
median5100
Q35500
95-th percentile5900
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation470.62042
Coefficient of variation (CV)0.09215871
Kurtosis-0.081387696
Mean5106.6298
Median Absolute Deviation (MAD)300
Skewness0.01983363
Sum924300
Variance221483.58
MonotonicityNot monotonic
2023-09-23T00:19:30.432227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4800 34
18.8%
5500 33
18.2%
5000 24
13.3%
5200 21
11.6%
5400 9
 
5.0%
6000 8
 
4.4%
5800 6
 
3.3%
5250 6
 
3.3%
4500 5
 
2.8%
4200 5
 
2.8%
Other values (12) 30
16.6%
ValueCountFrequency (%)
4150 5
 
2.8%
4200 5
 
2.8%
4250 2
 
1.1%
4350 4
 
2.2%
4400 3
 
1.7%
4500 5
 
2.8%
4650 1
 
0.6%
4750 4
 
2.2%
4800 34
18.8%
4900 1
 
0.6%
ValueCountFrequency (%)
6600 1
 
0.6%
6000 8
 
4.4%
5900 3
 
1.7%
5800 6
 
3.3%
5600 1
 
0.6%
5500 33
18.2%
5400 9
 
5.0%
5300 1
 
0.6%
5250 6
 
3.3%
5200 21
11.6%

city_mpg
Real number (ℝ)

Distinct27
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.132597
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:30.482965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.4182109
Coefficient of variation (CV)0.25537397
Kurtosis1.0227113
Mean25.132597
Median Absolute Deviation (MAD)5
Skewness0.74213901
Sum4549
Variance41.193432
MonotonicityNot monotonic
2023-09-23T00:19:30.534168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
31 24
13.3%
19 23
12.7%
24 22
12.2%
27 14
 
7.7%
17 11
 
6.1%
26 11
 
6.1%
23 10
 
5.5%
25 8
 
4.4%
28 7
 
3.9%
30 7
 
3.9%
Other values (17) 44
24.3%
ValueCountFrequency (%)
13 1
 
0.6%
14 2
 
1.1%
15 3
 
1.7%
16 4
 
2.2%
17 11
6.1%
18 3
 
1.7%
19 23
12.7%
20 3
 
1.7%
21 6
 
3.3%
22 4
 
2.2%
ValueCountFrequency (%)
49 1
 
0.6%
47 1
 
0.6%
45 1
 
0.6%
38 5
 
2.8%
37 5
 
2.8%
36 1
 
0.6%
33 1
 
0.6%
32 1
 
0.6%
31 24
13.3%
30 7
 
3.9%

highway_mpg
Real number (ℝ)

Distinct30
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.646409
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:30.589117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8885453
Coefficient of variation (CV)0.22477496
Kurtosis0.69316239
Mean30.646409
Median Absolute Deviation (MAD)5
Skewness0.58690397
Sum5547
Variance47.452056
MonotonicityNot monotonic
2023-09-23T00:19:30.642302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 18
 
9.9%
32 16
 
8.8%
30 16
 
8.8%
24 15
 
8.3%
38 15
 
8.3%
34 12
 
6.6%
37 11
 
6.1%
33 9
 
5.0%
28 9
 
5.0%
29 8
 
4.4%
Other values (20) 52
28.7%
ValueCountFrequency (%)
16 2
 
1.1%
17 1
 
0.6%
18 2
 
1.1%
19 2
 
1.1%
20 2
 
1.1%
22 5
 
2.8%
23 7
 
3.9%
24 15
8.3%
25 18
9.9%
26 3
 
1.7%
ValueCountFrequency (%)
54 1
 
0.6%
53 1
 
0.6%
50 1
 
0.6%
47 2
 
1.1%
46 2
 
1.1%
43 2
 
1.1%
42 2
 
1.1%
41 3
 
1.7%
39 1
 
0.6%
38 15
8.3%

price
Real number (ℝ)

Distinct168
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13271.315
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-09-23T00:19:30.704457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6229
Q17775
median10295
Q316500
95-th percentile34028
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation8106.4796
Coefficient of variation (CV)0.61082716
Kurtosis3.2006741
Mean13271.315
Median Absolute Deviation (MAD)3204
Skewness1.8284454
Sum2402108
Variance65715012
MonotonicityNot monotonic
2023-09-23T00:19:30.766776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7775 2
 
1.1%
6229 2
 
1.1%
5572 2
 
1.1%
18150 2
 
1.1%
7295 2
 
1.1%
13499 2
 
1.1%
16500 2
 
1.1%
7609 2
 
1.1%
8921 2
 
1.1%
8845 2
 
1.1%
Other values (158) 161
89.0%
ValueCountFrequency (%)
5118 1
0.6%
5151 1
0.6%
5195 1
0.6%
5389 1
0.6%
5399 1
0.6%
5499 1
0.6%
5572 2
1.1%
6095 1
0.6%
6229 2
1.1%
6295 1
0.6%
ValueCountFrequency (%)
45400 1
0.6%
41315 1
0.6%
40960 1
0.6%
37028 1
0.6%
36880 1
0.6%
36000 1
0.6%
35550 1
0.6%
35056 1
0.6%
34184 1
0.6%
34028 1
0.6%

Interactions

2023-09-23T00:19:26.098950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:11.586482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:12.829919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:13.662401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:14.525452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:15.378883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:16.491721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:17.324568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.161268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.990151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:19.815216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:20.648817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:21.849755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:22.728523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:23.530989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:24.345574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:25.221941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:26.151203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:11.658044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:12.878418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:13.714299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:14.576132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:15.429184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:16.540643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:17.373439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.210221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:19.038346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:19.864922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:21.006760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:21.900712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:22.775910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:23.578695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:24.396036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:25.273628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:26.200755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:11.706274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:12.923354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:13.761913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:14.623430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:15.476719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:16.586989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:17.419719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.258386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:19.083940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-09-23T00:19:16.334381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:17.168814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.006654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.837963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:19.663761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:20.495279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:21.683456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:22.569208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:23.383845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:24.197271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:25.056671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:25.937094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:27.250864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:12.725171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:13.560758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:14.418494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:15.273183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:16.385702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:17.218640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.056409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.887351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:19.713080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:20.545262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:21.736181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:22.620777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:23.431400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:24.245808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:25.111108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:25.989286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:27.305449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:12.777578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:13.611201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:14.471743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:15.325499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:16.438722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:17.272110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.108287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:18.938372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:19.764039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:20.596635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:21.795021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:22.674386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:23.481193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:24.295308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:25.167298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-23T00:19:26.043099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-23T00:19:30.842629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
IDsymbolingnormalized_losseswheel_baselengthwidthheightcurb_weightengine_sizeborestrokecompression_ratiohorsepowerpeak_rpmcity_mpghighway_mpgpricemakefuel_typeaspirationnum_of_doorsbody_styledrive_wheelsengine_locationengine_typenum_of_cylindersfuel_system
ID1.0000.052-0.008-0.114-0.108-0.089-0.004-0.080-0.031-0.100-0.076-0.060-0.0860.0480.0410.044-0.1040.0000.2090.1610.0000.0000.0000.0000.0080.0000.108
symboling0.0521.0000.509-0.549-0.404-0.247-0.540-0.233-0.150-0.120-0.015-0.0080.0380.293-0.0710.004-0.1180.4340.2240.1870.4750.3350.2700.2700.2260.1610.263
normalized_losses-0.0080.5091.000-0.114-0.0080.096-0.3760.0800.084-0.0580.143-0.0530.2070.256-0.220-0.1700.1530.3180.0470.0700.2630.1600.2330.0000.3170.1970.089
wheel_base-0.114-0.549-0.1141.0000.9140.8140.6280.7450.6320.5140.268-0.0610.474-0.330-0.438-0.4920.6580.4930.4030.3480.2810.3220.4020.5620.3810.3450.223
length-0.108-0.404-0.0080.9141.0000.8910.5120.8810.7780.6310.228-0.1340.645-0.297-0.631-0.6620.7990.4850.1550.2100.2140.2290.3980.0000.3460.3810.301
width-0.089-0.2470.0960.8140.8911.0000.3440.8530.7630.5990.268-0.0970.678-0.230-0.655-0.6750.8170.5450.3000.3370.1240.1400.4310.0500.4030.5700.256
height-0.004-0.540-0.3760.6280.5120.3441.0000.3280.1850.200-0.0080.046-0.018-0.283-0.026-0.0880.2310.4640.3230.2860.3720.5100.3810.2940.4180.3580.299
curb_weight-0.080-0.2330.0800.7450.8810.8530.3281.0000.8760.6940.191-0.1640.803-0.255-0.793-0.8190.9150.5010.3410.3800.1660.2510.4560.1050.3510.5030.284
engine_size-0.031-0.1500.0840.6320.7780.7630.1850.8761.0000.6880.303-0.2000.814-0.288-0.713-0.7060.8260.5230.1690.2930.0960.2210.4920.7430.5650.6630.325
bore-0.100-0.120-0.0580.5140.6310.5990.2000.6940.6881.000-0.078-0.1200.627-0.325-0.594-0.6040.6390.5760.1690.3300.0000.1790.4330.3360.3680.2350.364
stroke-0.076-0.0150.1430.2680.2280.268-0.0080.1910.303-0.0781.000-0.0760.159-0.072-0.054-0.0460.1510.6000.3380.2760.0000.1680.3390.7430.4450.2660.290
compression_ratio-0.060-0.008-0.053-0.061-0.134-0.0970.046-0.164-0.200-0.120-0.0761.000-0.320-0.0310.4330.395-0.1240.5070.9920.5570.1380.0000.1170.0000.3660.5300.516
horsepower-0.0860.0380.2070.4740.6450.678-0.0180.8030.8140.6270.159-0.3201.0000.098-0.912-0.8860.8490.4460.1040.3540.0240.2080.4380.8400.4450.5420.325
peak_rpm0.0480.2930.256-0.330-0.297-0.230-0.283-0.255-0.288-0.325-0.072-0.0310.0981.000-0.133-0.053-0.0830.4670.5840.3210.1320.0550.2470.4660.3630.3010.372
city_mpg0.041-0.071-0.220-0.438-0.631-0.655-0.026-0.793-0.713-0.594-0.0540.433-0.912-0.1331.0000.968-0.8220.3500.3550.2560.0580.0000.3690.1230.2620.4610.285
highway_mpg0.0440.004-0.170-0.492-0.662-0.675-0.088-0.819-0.706-0.604-0.0460.395-0.886-0.0530.9681.000-0.8200.3880.3600.3380.1790.0000.4230.0970.3770.5270.339
price-0.104-0.1180.1530.6580.7990.8170.2310.9150.8260.6390.151-0.1240.849-0.083-0.822-0.8201.0000.3820.3780.4010.0000.2350.4540.4910.2770.4530.282
make0.0000.4340.3180.4930.4850.5450.4640.5010.5230.5760.6000.5070.4460.4670.3500.3880.3821.0000.3920.4420.1590.3070.5950.7950.6740.5350.540
fuel_type0.2090.2240.0470.4030.1550.3000.3230.3410.1690.1690.3380.9920.1040.5840.3550.3600.3780.3921.0000.3770.1880.1640.1460.0000.3130.2080.983
aspiration0.1610.1870.0700.3480.2100.3370.2860.3800.2930.3300.2760.5570.3540.3210.2560.3380.4010.4420.3771.0000.0000.0000.1110.0000.2160.1770.625
num_of_doors0.0000.4750.2630.2810.2140.1240.3720.1660.0960.0000.0000.1380.0240.1320.0580.1790.0000.1590.1880.0001.0000.5310.0800.1080.0740.0540.141
body_style0.0000.3350.1600.3220.2290.1400.5100.2510.2210.1790.1680.0000.2080.0550.0000.0000.2350.3070.1640.0000.5311.0000.2460.4340.1290.0980.144
drive_wheels0.0000.2700.2330.4020.3980.4310.3810.4560.4920.4330.3390.1170.4380.2470.3690.4230.4540.5950.1460.1110.0800.2461.0000.1380.4440.3330.386
engine_location0.0000.2700.0000.5620.0000.0500.2940.1050.7430.3360.7430.0000.8400.4660.1230.0970.4910.7950.0000.0000.1080.4340.1381.0000.4000.2980.000
engine_type0.0080.2260.3170.3810.3460.4030.4180.3510.5650.3680.4450.3660.4450.3630.2620.3770.2770.6740.3130.2160.0740.1290.4440.4001.0000.5850.427
num_of_cylinders0.0000.1610.1970.3450.3810.5700.3580.5030.6630.2350.2660.5300.5420.3010.4610.5270.4530.5350.2080.1770.0540.0980.3330.2980.5851.0000.374
fuel_system0.1080.2630.0890.2230.3010.2560.2990.2840.3250.3640.2900.5160.3250.3720.2850.3390.2820.5400.9830.6250.1410.1440.3860.0000.4270.3741.000

Missing values

2023-09-23T00:19:27.405982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-23T00:19:27.644653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDsymbolingnormalized_lossesmakefuel_typeaspirationnum_of_doorsbody_styledrive_wheelsengine_locationwheel_baselengthwidthheightcurb_weightengine_typenum_of_cylindersengine_sizefuel_systemborestrokecompression_ratiohorsepowerpeak_rpmcity_mpghighway_mpgprice
01-165.00000toyotagasstdfourhatchbackfwdfront102.40000175.6000066.5000053.900002458ohcfour122mpfi3.310003.540008.7000092.000004200.00000273211248
120188.00000bmwgasstdtwosedanrwdfront101.20000176.8000064.8000054.300002710ohcsix164mpfi3.310003.190009.00000121.000004250.00000212820970
230115.00000renaultgasstdfourwagonfwdfront96.10000181.5000066.5000055.200002579ohcfour132mpfi3.460003.900008.7000095.000005100.0000023319295
340161.00000peugotdieselturbofoursedanrwdfront107.90000186.7000068.4000056.700003197lfour152idi3.700003.5200021.0000095.000004150.00000283313200
45-2103.00000volvogasstdfoursedanrwdfront104.30000188.8000067.2000056.200002935ohcfour141mpfi3.780003.150009.50000114.000005400.00000242815985
561118.00000dodgegasturbotwohatchbackfwdfront93.70000157.3000063.8000050.800002128ohcfour98mpfi3.030003.390007.60000102.000005500.0000024307957
670145.00000jaguargasstdfoursedanrwdfront113.00000199.6000069.6000052.800004066dohcsix258mpfi3.630004.170008.10000176.000004750.00000151932250
783115.00000porschegasstdtwohardtoprwdrear89.50000168.9000065.0000051.600002756ohcfsix194mpfi3.740002.900009.50000207.000005900.00000172532528
89089.00000subarugasstdfourwagonfwdfront97.00000173.5000065.4000053.000002455ohcffour108mpfi3.620002.640009.0000094.000005200.00000253110198
9103153.00000mitsubishigasturbotwohatchbackfwdfront96.30000173.0000065.4000049.400002370ohcfour110spdi3.170003.460007.50000116.000005500.0000023309959
IDsymbolingnormalized_lossesmakefuel_typeaspirationnum_of_doorsbody_styledrive_wheelsengine_locationwheel_baselengthwidthheightcurb_weightengine_typenum_of_cylindersengine_sizefuel_systemborestrokecompression_ratiohorsepowerpeak_rpmcity_mpghighway_mpgprice
171172198.00000chevroletgasstdtwohatchbackfwdfront94.50000155.9000063.6000052.000001874ohcfour902bbl3.030003.110009.6000070.000005400.0000038436295
1721733115.00000mitsubishigasturbotwohatchbackfwdfront95.90000173.2000066.3000050.200002833ohcfour156spdi3.580003.860007.00000145.000005000.00000192412629
1731741101.00000hondagasstdtwohatchbackfwdfront93.70000150.0000064.0000052.600001940ohcfour921bbl2.910003.410009.2000076.000006000.0000030346529
174175-174.00000volvogasstdfourwagonrwdfront104.30000188.8000067.2000057.500003034ohcfour141mpfi3.780003.150009.50000114.000005400.00000232813415
1751761125.00000mitsubishigasstdfoursedanfwdfront96.30000172.4000065.4000051.600002365ohcfour1222bbl3.350003.460008.5000088.000005000.0000025326989
1761771129.00000mazdagasstdtwohatchbackfwdfront98.80000177.8000066.5000053.700002385ohcfour1222bbl3.390003.390008.6000084.000004800.00000263210595
1771781113.00000mazdagasstdfoursedanfwdfront93.10000166.8000064.2000054.100001950ohcfour912bbl3.080003.150009.0000068.000005000.0000031387395
1781791119.00000plymouthgasstdtwohatchbackfwdfront93.70000157.3000063.8000050.800001918ohcfour902bbl2.970003.230009.4000068.000005500.0000037415572
1791803115.00000alfa-romerogasstdtwoconvertiblerwdfront88.60000168.8000064.1000048.800002548dohcfour130mpfi3.470002.680009.00000111.000005000.00000212716500
180181-1115.00000toyotagasstdfourwagonrwdfront104.50000187.8000066.5000054.100003151dohcsix161mpfi3.270003.350009.20000156.000005200.00000192415750